El Niño Impact on Regional Precipitation

Distribution patterns and mean values with confidence intervals

MakeoverMonday
Data Visualization
R Programming
2025
Analysis of precipitation anomalies during El Niño events compared to normal years across five global regions. This visualization reveals how El Niño Southern Oscillation affects rainfall patterns differently around the world, using complementary statistical approaches to show both distribution patterns and statistical significance.
Author

Steven Ponce

Published

March 18, 2025

Original

The original visualization Precipitation Anomalies comes from Our World in Data

Original visualization

Makeover

Figure 1: Side-by-side visualization showing El Niño’s impact on precipitation across five regions. The left side displays density plots showing distribution patterns; the right side shows mean values with confidence intervals. Data reveals contrasting precipitation patterns during El Niño vs normal years, with India showing the most dramatic differences.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  if (!require("pacman")) install.packages("pacman")
  pacman::p_load(
      tidyverse,      # Easily Install and Load the 'Tidyverse'
    ggtext,         # Improved Text Rendering Support for 'ggplot2'
    showtext,       # Using Fonts More Easily in R Graphs
    janitor,        # Simple Tools for Examining and Cleaning Dirty Data
    skimr,          # Compact and Flexible Summaries of Data
    scales,         # Scale Functions for Visualization
    glue,           # Interpreted String Literals
    here,           # A Simpler Way to Find Your Files
    lubridate,      # Make Dealing with Dates a Little Easier
    ggridges,       # Ridgeline Plots in 'ggplot2' # Ridgeline Plots in 'ggplot2'
    patchwork,      # The Composer of Plots
    camcorder       # Record Your Plot History 
  )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  12,
    height =  10,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code
#' The raw data for the week MakeoverMonday challenge can be downloaded 
#' https://data.world/makeovermonday/2025w12-precipitation-anomalies
#' 
#' Article
#' https://ourworldindata.org/grapher/global-precipitation-anomaly

precipitation_raw <- read_csv(
  here::here('data/global-precipitation-anomaly.csv')) |> 
    clean_names()

3. Examine the Data

Show code
glimpse(precipitation_raw)
skim(precipitation_raw)

4. Tidy Data

Show code
### |-  tidy data ----
# Define El Niño years based on moderate to strong events
el_nino_years <- c(1982, 1983, 1987, 1988, 1991, 1992, 1997, 1998, 
                   2002, 2003, 2009, 2010, 2015, 2016)

# ENSO (El Niño Southern Oscillation) data
enso_data <- precipitation_raw |>
  filter(entity %in% c('World', 'Brazil', 'Australia', 'India', 'United States')) |>
  mutate(
    el_nino = year %in% el_nino_years,
    event_type = if_else(el_nino, 'El Niño Year', 'Normal Year')
  )

# Comparative statistics for El Niño vs Normal
comparative_stats <- enso_data |>
  group_by(entity, event_type) |>
  summarize(
    mean_anomaly = mean(annual_precipitation_anomaly, na.rm = TRUE),
    median_anomaly = median(annual_precipitation_anomaly, na.rm = TRUE),
    sd_anomaly = sd(annual_precipitation_anomaly, na.rm = TRUE),
    n_obs = n(),
    se_anomaly = sd_anomaly / sqrt(n_obs),
    ci_lower = mean_anomaly - 1.96 * se_anomaly,
    ci_upper = mean_anomaly + 1.96 * se_anomaly,
    .groups = 'drop'
  )

5. Visualization Parameters

Show code
### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = c(
  'El Niño Year' = '#FF9800',
  'Normal Year' = '#2196F3'
))
  
### |-  titles and caption ----
title_text <- str_glue("El Niño Impact on Regional Precipitation")
subtitle_text <- str_glue("Distribution patterns (left) and mean values with confidence intervals (right)")

# Create caption
caption_text <- create_mm_caption(
    mm_year = 2025,
    mm_week = 12,
    source_text = "Our World in Data"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Weekly-specific modifications
    legend.position = "plot",

    axis.title = element_text(size = rel(1.14)),  
    axis.text = element_text(size = rel(1)),  

    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5), 
    axis.ticks.length = unit(0.2, "cm"), 

    panel.border = element_blank(),
    panel.grid = element_blank(),
    panel.spacing = unit(1, "lines"),  
    panel.spacing.y = unit(0, "lines"),
    
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code
### |-  Plot  ----
# P1. Ridges Chart ----
p1 <- ggplot(enso_data, aes(x = annual_precipitation_anomaly, y = entity, fill = event_type)) +
  # Geoms
  geom_density_ridges(
    scale = 0.9, 
    alpha = 0.7, 
    quantile_lines = TRUE,
    quantiles = c(0.5),  
    jittered_points = FALSE
  ) +
  geom_rug(
    aes(color = event_type), 
    alpha = 0.3, 
    size = 0.1,
    sides = "b",
    position = position_nudge(y = -0.2)  
  ) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "gray50") +
  # Scales
  scale_x_continuous() +
  scale_y_discrete() +
  scale_fill_manual(values = colors$palette) +
  scale_color_manual(values = colors$palette) +
  coord_cartesian(clip = 'off') +
  # Labs
  labs(
    x = 'Anomaly (mm)',
    y = NULL,
    fill = 'Year Type',
  )

# P2. Dot Plot ---- 
p2 <-ggplot(comparative_stats, aes(x = mean_anomaly, y = entity, color = event_type)) +
  # Geoms
  geom_vline(xintercept = 0, linetype = "dashed", color = "gray50", size = 0.7) +
  geom_errorbarh(
    aes(xmin = ci_lower, xmax = ci_upper),
    height = 0.3,
    size = 0.8,
    alpha = 0.7
  ) +
  geom_point(size = 3) +
  # Scales
  scale_x_continuous() +
  scale_y_discrete() +
  scale_color_manual(
    values = colors$palette
  ) +
  coord_cartesian(clip = 'off') +
  # Labs
  labs(
    x = "Mean Precipitation Anomaly (mm)",
    y = NULL,
    color = "Event Type: "
  ) +
  # Theme
  theme(legend.position = "top")

# Combined Plots ----
combined_plot <- (p1 | plot_spacer() | p2) +
  plot_layout(
    widths = c(1, 0.05, 1),
    nrow = 1) +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size   = rel(2.6),
        family = fonts$title,
        face   = "bold",
        color  = colors$title,
        lineheight = 1.1,
        margin = margin(t = 5, b = 5)
      ),
      plot.subtitle = element_text(
        size   = rel(1.1),
        family = fonts$subtitle,
        color  = colors$subtitle,
        lineheight = 1.2,
        margin = margin(t = 5, b = 15)
      ),
      plot.caption = element_markdown(
        size   = rel(0.75),
        family = fonts$caption,
        color  = colors$caption,
        hjust  = 0.5,
        margin = margin(t = 10)
      ),
      plot.margin = margin(t = 20, r = 10, b = 20, l = 10)
      )
    )

7. Save

Show code
### |-  plot image ----  
save_plot_patchwork(
  combined_plot, 
  type = "makeovermonday", 
  year = 2025,
  week = 12,
  width = 12, 
  height = 10
  )

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] camcorder_0.1.0 patchwork_1.3.0 ggridges_0.5.6  here_1.0.1     
 [5] glue_1.8.0      scales_1.3.0    skimr_2.1.5     janitor_2.2.0  
 [9] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
[13] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[17] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[21] ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          htmlwidgets_1.6.4  tzdb_0.4.0        
 [5] yulab.utils_0.1.8  vctrs_0.6.5        tools_4.4.0        generics_0.1.3    
 [9] curl_6.0.0         parallel_4.4.0     gifski_1.32.0-1    fansi_1.0.6       
[13] pkgconfig_2.0.3    ggplotify_0.1.2    lifecycle_1.0.4    compiler_4.4.0    
[17] farver_2.1.2       munsell_0.5.1      repr_1.1.7         codetools_0.2-20  
[21] snakecase_0.11.1   htmltools_0.5.8.1  yaml_2.3.10        crayon_1.5.3      
[25] pillar_1.9.0       magick_2.8.5       commonmark_1.9.2   tidyselect_1.2.1  
[29] digest_0.6.37      stringi_1.8.4      labeling_0.4.3     rsvg_2.6.1        
[33] rprojroot_2.0.4    fastmap_1.2.0      grid_4.4.0         colorspace_2.1-1  
[37] cli_3.6.3          magrittr_2.0.3     base64enc_0.1-3    utf8_1.2.4        
[41] withr_3.0.2        bit64_4.5.2        timechange_0.3.0   rmarkdown_2.29    
[45] bit_4.5.0          hms_1.1.3          evaluate_1.0.1     knitr_1.49        
[49] markdown_1.13      gridGraphics_0.5-1 rlang_1.1.4        gridtext_0.1.5    
[53] Rcpp_1.0.13-1      xml2_1.3.6         renv_1.0.3         vroom_1.6.5       
[57] svglite_2.1.3      rstudioapi_0.17.1  jsonlite_1.8.9     R6_2.5.1          
[61] fs_1.6.5           systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in mm_2025_12.qmd.

For the full repository, click here.

10. References

  1. Data:
  1. Article
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